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    Refining Ideas with Generative AI: How Text-and Image-Based Scenarios Influence Idea Refinement
    In the innovation process, generative AI can be used to refine ideas with text-based scenarios and image-based visualization. Our findings reveal that text-based scenarios versus image-based visualization scenarios enhance creative performance because they increase levels of mental outcome simulations.
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    LLM-Augmentation for Idea Evaluation: Developing a Reference Model for Evaluation Pipelines
    (Springer Nature Switzerland, 2025-05-27) ;
    Samir Chatterjee
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    Vom Brocke Jan
    ;
    Anderson, Ricardo
    Automated approaches to idea evaluation increasingly leverage generative artificial intelligence to support decision-makers. However, contextualizing evaluations within specific domains remains challenging, particularly at varying levels of large language model (LLM) augmentation. Existing research employs embeddings to derive semantic insights, yet these representations often lack domain-specific contextualization. Recent advancements, such as chat-based LLMs, present new opportunities to incorporate context through prompting. To address these challenges, we propose a structured evaluation pipeline that integrates embeddings with feature engineering to enhance the contextualization of chat-based LLM evaluations. Using a real-world innovation challenge, we instantiate this pipeline and assess its predictive performance across different levels of augmentation. Our findings reveal that incorporating contextual information improves predictive accuracy but depends on fine-grained idea quality dimensions. By codifying our approach into a reference model, we provide a transferable framework that generalizes across various evaluation contexts employing LLMs.
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    Generative AI in Idea Development: The Role of Numeric and Visual Feedback
    Human creativity is a crucial factor in developing innovative ideas. Many ideas are being generated, but only a few receive feedback, as creating feedback is a costly and timeconsuming effort in innovation. While feedback promises higher idea quality, previous work requires human experts with domain expertise. Generative AI could provide automated feedback and is expected to transform creative work. This short paper presents an experimental series in which we let humans collaborate with generative AI to develop ideas. Based on dual-coding and media synchronicity theory, we conceptualize numerical and visual feedback to overcome cognitive barriers. We manipulate feedback modalities and timing to personalize the interaction. Our contributions provide evidence on when and why specific co-creative arrangements between humans and generative AI are favorable.
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